Design and development of organic semiconducting materials for solar cells

光伏（PV）允许将阳光直接转换为电能。它是在我们实现净零碳排放的竞赛中使用的最重要的可再生能源技术之一。虽然晶体硅PV （c-Si PV）是迄今为止在许多应用中最有效和最具成本效益的技术，但由于c-Si本身就很重、易碎且不透明，因此在某些应用中硅根本不适用。因此需要替代的光伏技术。有机PV （OPV）是一种非常有前途的互补技术，因为有机半导体的材料特性与c-Si PV截然不同，它们重量轻，柔性和半透明，并且与高吞吐量卷对卷加工兼容。在弱光条件下，OPV的性能也优于c-Si PV。OPV的一些小众应用包括建筑集成光伏、便携式电子、物联网和航空航天。近年来，OPV的效率几乎翻了一番，由于所谓的非富勒烯电子受体有机合成的最新进展，OPV的效率接近20%。如果我们想要超过20%的效率并接近理论极限，就需要做更多的工作来了解关键的分子和电子过程。我们需要更深入地了解固态分子的电子结构，以及局部分子间和分子内相互作用如何影响器件中的电子转移和输运。将这些拼凑在一起将允许获得新的设计规则，然后将其应用于产生设备性能的重大进步。在这个项目中，我们将利用计算和实验方法的结合，首先了解最先进系统的现状，然后制定设计规则，以实现创纪录的效率OPV器件。为此，候选人将应用基态和时变密度泛函理论（DFT）方法，分子动力学（MD）模拟和深度学习（DL）算法，以协助新的有机半导体材料的实验开发。使用先进的高通量和gpu加速的超级计算进行多尺度建模和模拟（在不同长度尺度上集成DFT和MD），以及DL神经网络的应用有望通过缩小设计空间，解锁结构-性质关系，甚至通过发现意想不到的分子设计来加速OPV材料的开发。

Photovoltaics (PV) allows for the direct conversion of sunlight to electricity. It is one of the most important renewable energy technologies being utilised in our race towards net-zero carbon emissions. While crystalline silicon PV (c-Si PV) is the most efficient and cost-effective technology to date for many applications, there are certain applications in which silicon is simply not applicable due to c-Si being inherently heavy, brittle, and opaque. Alternative PV technologies are thus required. Organic PV (OPV) is one very promising and complimentary technology to c-Si PV due to the contrasting material properties of organic semiconductors in that they are light-weight, flexible and semi-transparent and compatible with high-throughput roll-to-roll processing. OPV also outperforms c-Si PV in low-light conditions. Some of the niche applications of OPV include building integrated PV, portable electronics, internet-of-things, and aerospace. OPV efficiencies have almost doubled in recent years and are closing in on 20% thanks to recent advances in organic synthesis of so-called non-fullerene electron acceptors. If we are to exceed 20% efficiencies and move closer to the theoretical limit, more work is needed to understand the key molecular and electronic processes at play. We need a deeper understanding of the electronic structure of the molecules in the solid-state and how local inter- and intra-molecular interactions affect electron transfer and transport in the device. Piecing these together will allow for new design rules to be obtained, which will then be applied to produce significant advances in device performance. In this project, we will utilise a combination of computational and experimental approaches to first understand the current state of the art systems and then develop design rules for achieving record efficiency OPV devices. To this end, the candidate will apply ground-state and time-dependent Density Functional Theory (DFT) methods, molecular dynamics (MD) simulations, and deep learning (DL) algorithms, to assist the experimental development of new organic semiconducting materials. The use of advanced high-throughput and GPU-accelerated supercomputing for multiscale modelling and simulations (integrating DFT and MD across different length scales), and the application of DL neural networks is expected to speed up the development of OPV materials, by narrowing down the design space, unlocking structure-property relationships, and even more, by discovering unexpected molecular designs.